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Record W3080871232 · doi:10.1249/fit.0000000000000593

2019 PAPER OF THE YEAR

2020· article· en· W3080871232 on OpenAlexaboutno aff
Peter Ronai

Bibliographic record

VenueACSMʼs Health & Fitness Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsCertificationMedical educationResistance (ecology)PublishingPsychologyPopulationGerontologyMedicinePolitical science

Abstract

fetched live from OpenAlex

ACSM’s Publications Committee is pleased to highlight the excellent content its journals are publishing and acknowledges authors whose exemplary work provides readers with unique scientific and practical content. The editorial team of ACSM’s Health & Fitness Journal® has selected “Developing a Lifelong Resistance Training Program,” by Len R. Kravitz, Ph.D. (1), as the 2019 Paper of the Year. Resistance training has been identified as an evidence-based, effective, and vital modality for enhancing health-related physical fitness and function and a recommended component of comprehensive exercise programs for people of all ages (2–5). Len’s article was selected because it has so clearly translated the scientific literature and industry guidelines on resistance training into practical information, which readers can apply immediately with clients and patients. Len distinctly summarizes current science behind the recommendations and provides meaningful, practical examples that readers can easily follow. His descriptions of best practices are consistent with recommendations for the general population (2,3), athletes (4), youth (3,5), older adults (6–9), and select populations (10–13). He provides easy to follow recommendations for designing safe and effective resistance training programs for clients and patients throughout the life span. Students, faculty, health and fitness professionals, and ACSM certification candidates can use this article as a primer. Len is an accomplished, articulate, and highly sought health and fitness industry educator and expert. Len’s articles and keynote presentations are always exceptionally informative, practical, and engaging. His knowledge and expertise as a scientist, researcher, and author is second only to his energy, enthusiasm, and passion for teaching. Len is program coordinator of Exercise Science and a researcher at the University of New Mexico where he won the Outstanding Teacher of the Year award. For his distinguished service, Len was inducted into the National Health and Fitness Hall of Fame Museum and Institute and has been recognized as the 2009 Canadian Fitness Professional “Specialty Presenter of the Year,” American Council on Exercise (ACE) 2006 “Fitness Educator of the Year,” and has received the Canadian Fitness Professional “Lifetime Achievement Award.” To hear more about this article from Len, click here: https://links.lww.com/FIT/A147. To read Len's Paper of the Year visit: https://journals.lww.com/acsm-healthfitness/Fulltext/2019/01000/DEVELOPING_A_LIFELONG_RESISTANCE_TRAINING_PROGRAM.6.aspx.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.318
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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